Presentation Transcript

BACKGROUND:

BACKGROUND

BACKGROUND CONT’D…:

BACKGROUND CONT’D… This research aims to build a recreation demand model for a natural area
in Queensland, in order to evaluate the effects of alternative management
policies.
In particular, the model will attempt to address the issue of overcrowding and
estimate the welfare implications of using alternative methods to ration access
to natural areas.
The research will also look more closely at what is meant by ‘overcrowding’
or ‘congestion’ and how it affects visitor satisfaction.
But first, the literature…

FOUNDATIONS:

FOUNDATIONS UTILITARIANISM
Originating in the works of David Hume (1711-1776) and Jeremy Bentham
(1748-1832), and more completely expressed in the works of John Stuart Mills
(1806-1873), in particular Utilitarianism (1863).
NEOCLASSICAL TRADITION
Theories of consumer preferences, utility, demand and marginal analysis
formalised by William Jevons (1835-1883) and Carl Menger (1840-1921)
WELFARE ECONOMICS
In particular Alfred Marshall (1842-1924) and his Principles of Economics
(1890) and Arthur Cecil (A..C.) Pigou (1877-1959) in his The Economics of
Welfare (1920).

ENVIRONMENTAL VALUATION:

ENVIRONMENTAL VALUATION CONSUMERS’ SURPLUS
Used by Marshall for welfare analysis. John Hicks (1941) developed a set of
money measures of utility changes – ‘compensating’ and ‘equivalent’
variation.
.
NON-MARKET VALUATION
A number of methods; the two most common are the Travel Cost Method (RP)
and Contingent Valuation (SP)

ENVIRONMENTAL VALUATION CONT’D…:

ENVIRONMENTAL VALUATION CONT’D…

RECREATION DEMAND MODELS:

RECREATION DEMAND MODELS PARTICIPATION MODELS
Use quantity of visits to fit a
standard neoclassical
(Marshallian) demand
function.
(The Clawson-Knetsch approach)
SITE SELECTION MODELS
Use discrete choice models to
model site selection based on
Random Utility Maximisation
(RUM) (McFadden, 1974;
Hanemann, 1978) Revealed preference techniques are preferred by some because they are based
on actual behaviour. Restricting our attention to this branch of the literature.
Hotelling’s letter and Clawson and Knetsch’s subsequent papers have lead to a
rich literature on modelling demand for recreation sites. They can be split into
two types.

Slide8:

Cameron (1992), using a participation model, was the first to combine stated
and revealed preference data. Adamowicz et al. (1994) use the idea in a RUM
model. Several authors (Kling, 1997; McConnell et al., 1999) have followed
their lead.
Hanley et al. (2002) use a RUM model to consider alternative means of
rationing access to outdoor recreation areas. In a similar vein, Grijalva et al.
(2002) use a national level RUM model to estimate welfare losses resulting
from access restrictions to rock climbing sites in the U.S.
Bateman et al. (2003) consider the use of Geographical Information Systems
(GIS) in travel cost analysis.
RECENT INNOVATIONS

Slide9:

Fisher and Krutilla (1972) seek to maximise recreation benefits in an area
subject to congestion. Cicchetti and Smith (1973; 1976) use a stated preference
survey to estimate the effect of congestion on willingness to pay for
wilderness experiences.
Freeman and Haveman (1977) introduce heterogeneous tastes for congestion.
McConnell (1977) models beach congestion. Cesario (1980)
considers the value of establishing new sites in relieving the congestion of
existing sites.
More recently, Jakus and Shaw (1997) incorporate a congestion measure into a
model using revealed preference data. Boxall et al. (2003) demonstrate that the
welfare effects of congestion vary across areas, stages of a trip, and
individuals.
MODELLING CONGESTION

Slide10:

LEISURE LITERATURE Wagar (1964) introduced the concept of carrying capacity for recreational
sites.
Limits of Acceptable Change were put forward by Stankey et al. (1985) as an
alternative to carrying capacity. This has become the preferred approach.
Numerous studies attempt to link number of encounters with visitor
experience quality (Stankey, 1973; Shelby, 1980). Overall the findings have
been mixed. Stewart and Cole (2001), using a diary method, find that the
relationship is negative but weak, implying use restrictions are hard to justify
on the basis of enhanced visitor experience alone.
NOTE: The transport literature (unsurprisingly) also addresses congestion.

Slide11:

The intended approach is to use a combination of revealed and stated
preference data to estimate a recreation demand model of a case study site
(yet to be determined) in Queensland. The choice of site (for example whether
it is a single site with few substitutes or one of a series of sites) will in part
determine the choice of model.
This will involve an on-site survey of visitors, including questions designed to
elicit a measure of visitors’ perceptions of congestion.
Once a model has been estimated, the effect of alternative management
policies will be simulated. It is hoped to be able to provide useful information
to resource managers, to enable them to better understand the issues
surrounding natural areas experiencing high levels of visitation.
METHODOLOGY

Slide12:

METHODOLOGY (SINGLE SITE) If a single site is chosen, a standard demand function approach estimating
recreation demand could be appropriate.
The frequency of visits for person ‘i’ to the site can be given by:
Where Pi is the “price” to visitor ‘i’ of visiting the site and Zi is a vector of
the visitor’s characteristics.
If more than one site is chosen, however, a Random Utility Maximisation
(RUM) approach may be more appropriate.

Slide13:

METHODOLOGY (MULTIPLE SITES)
Random utility theory considers utility to be a random variable, partly
observable and partly not. The utility gained by visitor ‘i’ to site ‘j’ can
be represented as follows:
Where the observed component contains a vector of the characteristics of the
site (X) and of the individual (Z):
The probability that site ‘j’ will be visited by visitor ‘i’ is equal to the
probability that the utility gained from visiting ‘j’ is greater than or equal to
that gained from visiting any other site ‘k’ in a finite set ‘C’.

Slide14:

A number of possibilities, for example Jakus and Shaw posit two functions for
a visitor’s demand for a recreational site as follows:
and,
Where P represents the “price” of visiting a site, C is congestion, A a vector of
site attributes and Y the visitor’s recreation income.
In the former, the congestion term has as a direct utility effect, and therefore
must appear in the visitor’s utility function. In the latter, congestion is viewed
as part of the “price” of visiting the site (for example by increasing the amount
of time needed to recreate).
METHODOLOGY (INCORPORATING CONGESTION)

Slide15:

In addition to addressing a real ‘on the ground’ policy issue in Queensland, I
hope to make a contribution to the fields of non-market valuation and
natural resource management by doing the following:
Reconsidering the definition and treatment of congestion in revealed
preference models. An area of significant debate and of increasing
importance to the management and conservation of protected natural areas.
Providing a systematic analysis of alternative rationing tools. Although the role of pricing has received extensive attention, other methods deserve further examination.
Finally, the issue of rationing access to natural areas is often an emotive one, it
is hoped that this research will promote a more rational debate on the subject.
MY CONTRIBUTION